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workflow_run

Run a sequence of browser automation steps (navigate, click, fill, etc.) with error handling and resume from a specific step if failure occurs.

Instructions

⭐ Execute a list of tool steps sequentially. Resumable — pass start_at=N to skip the first N steps.

Each step: {"tool": "<name>", "args": {...}, "label": "optional"}

Args:
    steps: list of step dicts
    start_at: index to begin from (for resume after a fix)
    stop_on_error: abort on first failure (default True). If False,
        continue and collect all results.

Returns JSON:
  {
    "completed": [
      {"index": 0, "tool": "navigate", "ok": true, "result": "..."},
      ...
    ],
    "failed_at": 3,                # index of failure (omitted on success)
    "failure_context": {...},      # last step's input + error (for LLM debug)
    "resume_with": "workflow_run(steps=..., start_at=4)"  # hint
  }

Allowed tools (curated for sequencing): navigate, reload, go_back/forward,
click, click_text, click_role, fill, type_text, press_key, select_option,
check, uncheck, wait_for*, screenshot, scroll, scroll_to, smart_fill,
vision_locate, assert_*, storage_*, cookie_import, storage_state_load,
evaluate, mouse_click_xy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stepsYes
start_atNo
stop_on_errorNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It describes sequential execution, resumability, start_at, stop_on_error behavior, and detailed return format with resume hint. Covers all key behavioral aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with emoji, bullet points, and code blocks. Front-loaded with main purpose. Every sentence is informative, no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given complexity and lack of annotations, description is complete: covers input, behavior, allowed tools, return format, and resume hint. Output schema details are provided inline, so no gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, description adds significant value by explaining step format (dictionary with tool, args, label) and clarifying start_at and stop_on_error semantics. Schema already has types and defaults, but description provides needed context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it executes a list of tool steps sequentially, with resumability. It distinguishes from siblings by listing allowed tools for sequencing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explains when to use (sequential execution of allowed tools) and how to resume with start_at. Does not explicitly state when not to use, but allowed tools list provides implicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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